cleanup no required install

README.md

@@ -247,7 +247,7 @@ rag = LightRAG(
 
 * If you want to use Hugging Face models, you only need to set LightRAG as follows:
 
-See lightrag_hf_demo.py
+See `lightrag_hf_demo.py`
 
 ```python
 from lightrag.llm import hf_model_complete, hf_embed

lightrag/api/requirements.txt

@@ -1,9 +1,9 @@
+aiofiles
 ascii_colors
 fastapi
 numpy
 pipmaster
 python-dotenv
-python-multipart
 tenacity
 tiktoken
 uvicorn

lightrag/kg/milvus_impl.py

@@ -6,12 +6,16 @@ import numpy as np
 from lightrag.utils import logger
 from ..base import BaseVectorStorage
 import pipmaster as pm
-import configparser
+
+
+if not pm.is_installed("configparser"):
+    pm.install("configparser")
 
 if not pm.is_installed("pymilvus"):
     pm.install("pymilvus")
 
 try:
+    import configparser
     from pymilvus import MilvusClient
 except ImportError as e:
     raise ImportError(

lightrag/kg/networkx_impl.py

@@ -1,7 +1,6 @@
-import html
 import os
 from dataclasses import dataclass
-from typing import Any, cast, final
+from typing import Any, final
 
 import numpy as np
 
@@ -14,8 +13,16 @@ from lightrag.utils import (
 from lightrag.base import (
     BaseGraphStorage,
 )
+import pipmaster as pm
+
+if not pm.is_installed("graspologic"):
+    pm.install("graspologic")
+
+if not pm.is_installed("networkx"):
+    pm.install("networkx")
 
 try:
+    from graspologic import embed
     import networkx as nx
 except ImportError as e:
     raise ImportError(
@@ -39,21 +46,6 @@ class NetworkXStorage(BaseGraphStorage):
         )
         nx.write_graphml(graph, file_name)
 
-    @staticmethod
-    def stable_largest_connected_component(graph: nx.Graph) -> nx.Graph:
-        """Refer to https://github.com/microsoft/graphrag/index/graph/utils/stable_lcc.py
-        Return the largest connected component of the graph, with nodes and edges sorted in a stable way.
-        """
-        from graspologic.utils import largest_connected_component
-
-        graph = graph.copy()
-        graph = cast(nx.Graph, largest_connected_component(graph))
-        node_mapping = {
-            node: html.unescape(node.upper().strip()) for node in graph.nodes()
-        }  # type: ignore
-        graph = nx.relabel_nodes(graph, node_mapping)
-        return NetworkXStorage._stabilize_graph(graph)
-
     @staticmethod
     def _stabilize_graph(graph: nx.Graph) -> nx.Graph:
         """Refer to https://github.com/microsoft/graphrag/index/graph/utils/stable_lcc.py
@@ -153,8 +145,6 @@ class NetworkXStorage(BaseGraphStorage):
 
     # @TODO: NOT USED
     async def _node2vec_embed(self):
-        from graspologic import embed
-
         embeddings, nodes = embed.node2vec_embed(
             self._graph,
             **self.global_config["node2vec_params"],

lightrag/kg/oracle_impl.py

@@ -20,10 +20,14 @@ from ..utils import logger
 
 import pipmaster as pm
 
+if not pm.is_installed("graspologic"):
+    pm.install("graspologic")
+
 if not pm.is_installed("oracledb"):
     pm.install("oracledb")
 
 try:
+    from graspologic import embed
     import oracledb
 
 except ImportError as e:
@@ -452,8 +456,6 @@ class OracleGraphStorage(BaseGraphStorage):
 
     async def _node2vec_embed(self):
         """为节点生成向量"""
-        from graspologic import embed
-
         embeddings, nodes = embed.node2vec_embed(
             self._graph,
             **self.config["node2vec_params"],

requirements.txt

@@ -1,12 +1,6 @@
-accelerate
-aiofiles
 aiohttp
 configparser
 
-# File manipulation libraries
-docling
-graspologic
-
 # database packages
 networkx